Monte Carlo SHALSTAB: A probabilistic-based SHALSTAB Analysis

Abstract

This paper aims to propose a method for assessing slope stability through probabilities, which can support sustainability based on an understanding of land use and land cover. The method uses the SHALSTAB mathematical model as a deterministic basis and, in order to take into account uncertainties, applies the Monte Carlo method in conjunction with probability density functions. Deterministic methods alone consider the events and parameters to be unique, as if no randomness exists. The events and combinations of soil parameters that generate instabilities are random, and for this reason the proposed method achieved optimal results. In general, the use of mean values for the parameters is used in deterministic modelling, but these mean values do not represent the continuous variation existing in the field, and there is also a great chance that the applied means do not summarize the study area correctly. Monte Carlo relies on the law of large numbers that will tend to the average probability after several simulations, and for this reason stochasticity carries more powerful information than determinism. A total of 100,000 SHALSTAB simulations were run, varying in each iteration the geomechanical parameters of the soils, soil depth and saturated hydraulic conductivity, as results, the calculated statistical AUC (Area Under the ROC Curve), used to validate the method, was 0.887.